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检索条件"机构=Dept. of Computer Science and Biomedical Informatics"

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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Image-Based Path-Planning for Navigation of Soft-Growing Robots in the Spinal Sub-Arachnoid Space

Image-Based Path-Planning for Navigation of Soft-Growing Rob...

引用

IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： George Dimas Panagiotis G. Kalozoumis Panagiotis Vartholomeos Dimitris K. Iakovidis Dept. of Computer Science and Biomedical Informatics School of Science University of Thessaly Lamia Greece

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Recently, soft robotic procedures are being developed aiming at achieving targeted minimally invasive operations or drug administration in specific locations in the human body. Accordingly, specialized path-planning algorithms need to be investigated for the estimation of efficient paths that would protect the delicate tissue structures and simultaneously satisfy a set of requirements for the efficient navigation of the robot. In this context, we propose an image-based 3D path-planning algorithm, which is an extension of the A* algorithm, for the navigation of a soft-growing robot inside the spinal sub-arachnoid space (SSS). The proposed algorithm is capable of estimating a safe pathway towards the goal location, while ensuring the establishment of anchor points that facilitate the efficient steering of the soft robot. The algorithm is evaluated using a highly detailed model of the SSS with respect to its capacity to satisfy the robot movement requirements and path efficiency.

关键词： Minimally invasive surgery Three-dimensional displays Targeted drug delivery Navigation Magnetic resonance imaging Refining Phantoms Kinematics Soft robotics Cost function

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Application of Augmented Reality and The Floyd-Warshall Algorithm in The Kujang Pamor Labyrinth Educational Game

Application of Augmented Reality and The Floyd-Warshall Algo...

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Tosida, Eneng Tita Raafi'udin, Ridwan Ashar, Alfenia Rosyida Kurnia, Ema Pakuan Univeristy Computer Science Dept. Bogor Indonesia Upn Veteran Informatic Dept. Jakarta Indonesia Pakuan University Computer Science Dept. Bogor Indonesia Pakuan University Informatics Management Dept. Bogor Indonesia

ISBN: (纸本)9798350307306

The efforts that are being made to bolster indigenous knowledge for the m illennial generation need to be carried out using a technical strategy that is based on the industrial revolution 4.0 and is moving toward society 5.0. Kujangs, which have a variety of motifs on their b lades and carry cultural wisdom values, need to be socialized using a method that takes a millennial technology perspective in order to be able to transform the difficulties of s ociety 5.0 into the strengths of the Sustainable Development Goals ( SDGs). The purpose of this study is to develop an instructional game based on the kujang motif by utilizing Augmented Reality techniques and putting them into practice using the Floyd-Warshall Algorithm. The game of kujang pamor is designed on the idea of a maze, and the path through the maze is constructed on the basis of the cleaver figures. The complexity of each of s even different routes through a three-dimensional maze that uses augmented reality serves as the basis for the g ame's leveling system. In a maze game with clever figures, the Floyd-Warshall algorithm is possible to optimize the search for the path that is the shortest distance between two points. The originality of this study lies in the fact that it utilizes augmented reality techniques while simultaneously searching for the path with the least distance in a maze game based on the kujang pamor. © 2023 IEEE.

关键词： Augmented reality

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid 11

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Hosei University Dept. of Applied Informatics Tokyo Japan

ISBN: (纸本)9798350306941

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot's capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023 IEEE.

关键词： Visibility

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Development and evaluation of a deep learning model for oral soft and hard tissue diagnosis using synthetic image data generated by StyleGAN

Development and evaluation of a deep learning model for oral...

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Computational Optical Imaging and Artificial Intelligence in biomedical sciences 2024

作者： Jana, Ananya Jain, Vrinda Oliveira, Abmael Subhash, Hrebesh Molly Metaxas, Dimitris Dept. of Computer Science Alvernia University Reading United States Dept. of Biomedical Enginnering Rutgers University New Brunswick United States Colgate-Palmolive Company Piscataway United States Dept. of Computer Science Rutgers University Reading United States

ISBN: (纸本)9781510669734

Automatic intraoral imaging-based assessment of oral conditions is important for clinical and consumer-level oral health monitoring. But there is a lack of publicly available intraoral datasets. To address this, we developed a StyleGAN2-based framework to generate synthetic 2D intraoral images. The StyleGAN2 network was trained on 3724 images with a Frechet Inception Distance 12.10. Dental professionals evaluated image quality and determined if images were real or synthetic. Approximately 83.75% of generated images were deemed real. We created a framework that utilizes pseudo-labeling to incorporate the StyleGAN2-synthesized 2D intraoral images into a tooth type classification model. Our experiments demonstrated that the StyleGAN2 synthesized images can effectively augment the training set and improve the performance of the tooth type classification model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image classification

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Preprocessing Pathology Reports for Vision-Language Model Development

Preprocessing Pathology Reports for Vision-Language Model De...

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2024 MICCAI Workshop on Computational Pathology, MICCAI COMPAYL 2024

作者： Lucassen, Ruben T. van de Luijtgaarden, Tijn Moonemans, Sander P.J. Blokx, Willeke A.M. Veta, Mitko Dept. of Pathology University Medical Center Utrecht Netherlands Dept. of Biomedical Engineering Eindhoven University of Technology Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Pathology reports are increasingly being used for development of vision-language models. Because the reports often include information that cannot directly be derived from paired images, careful selection of information is required to prevent hallucinations in tasks like report generation. In this paper, we present a language model for subsentence segmentation based on the information content, as part of a preprocessing workflow for 27,500 pathology reports of cutaneous melanocytic lesions. After initial clean up, the reports were first translated from Dutch to English and then segmented by separate language models. Both models were developed using an iterative approach, in which the development dataset was expanded with manually corrected model predictions for previously unannotated reports before finetuning the next version of the models. Over the course of eight iterations, the development dataset was in the end scaled up to 1,500 translated and annotated reports. On the independent test set of 3,597 sentences from 150 reports, 219 translation errors (6,1%) of different severities were counted. The subsentence segmentation model achieved a strong predictive performance on the test set with a macro average F1-score of 0.921 (95% CI, 0.890-0.940) and a weighted average F1-score of 0.952 (95% CI, 0.944-0.960) over 13 different classes. The remaining 25,850 unannotated reports were translated and segmented using the final models to complete the dataset preprocessing. Differences in word count and class distribution between section types of the reports were explored in preparation for future vision-language modeling. The presented methodology is generic and can, therefore, easily be extended to multiple or different pathology domains beyond melanocytic skin lesions. Code and trained model parameters are made publicly available. ©2024 Lucassen et al.

关键词： Visual languages

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Private Continuous Survival Analysis with Distributed Multi-Site Data

Private Continuous Survival Analysis with Distributed Multi-...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Bonomi, Luca Lionts, Marilyn Fan, Liyue Vanderbilt University Medical Center Dept. Biomedical Informatics NashvilleTN United States Vanderbilt University Dept. Computer Science NashvilleTN United States University of North Carolina College of Computing and Informatics CharlotteNC United States

ISBN: (纸本)9798350324457

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results. © 2023 IEEE.

关键词： Data Privacy Distributed Data Survival Analysis

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Smart Beds Designed for Monitoring and Treatment of Pressure Ulcers 3

Smart Beds Designed for Monitoring and Treatment of Pressure...

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3rd IEEE International Conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Robinson, Sheryl Kumar, Aishwarya Mohan Bethanney Janney, J. Sivakumar, Poojahsri Dhanalakshmi, K. Prince, P. Grace Kanmani Sathyabama Institute of Science and Technology Dept. of Biomedical Engg Chennai India Sathyabama Institute of Science and Technology Dept. of Computer Science Engg Chennai India

ISBN: (纸本)9798350397819

One of the most common risks for individuals who are bedridden is the development of bedsores (Pressure Ulcers). The number of senior persons who are permanently disabled and semi-impaired is growing as the population ages. The conventional methods used to relieve bed sores tend to focus on the pressure distribution and very minimal focus is provided for therapy. In order to detect patterns of warning signs or pathogens that can be treated early to reduce morbidity and mortality as well as length and expense of hospitalization, it is crucial to continuously monitor the patient's health while they are in the hospital. Healthcare workers continuously monitor the patient's vital signs, making it simple and safe to shield the patient from unforeseen threats and to reduce harmful health issues. On the other hand, a major side effect of hospitalization is the impact of pressure ulcers on the bedridden patient as a result of strong shear forces. A pressure ulcer or bedsores is a damage to tissue that has been disturbed or lacks blood flow as a result of prolonged pressure and shear forces, and it can give the patient pain and discomfort. This review focuses on research articles that deals with monitoring as well as treatment of patients inflicted with pressure ulcers. Furthermore, we have also proposed a basic system and methodology that can be followed by others for future research work. © 2023 IEEE.

关键词： Hospitals

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Early Prediction of Depressive Episodes in Mood Disorders Us...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kim, Byeongsu Chae, Minsu Kim, Yihyun Kong, Seokjin Kim, Yeongmin Jung, Taewon Jeong, Jaegwon Park, Soohyun Cho, Chul-Hyun Yeom, Ji Won Lee, Taek Lee, Heon-Jeong Lee, Hwamin Korea University College of Medicine Dept. of Biomedical Informatics Seoul Korea Republic of Korea University College of Medicine Dept. of Psychiatry Seoul Korea Republic of Korea University Chronobiology Institute Seoul Korea Republic of SunMoon University Dept. of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350386226

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions. © 2024 IEEE.

关键词： Sleep research

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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